Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Susmita Datta received her PhD in statistics from the University of Georgia.  She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor in the Department of Bioinformatics and Biostatistics and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science (AAAS), American Statistical Association (ASA), and an elected member of the International Statistical Institute (ISI). Her research interests include bioinformatics, genomics, proteomics, clustering and classification techniques, infectious disease modeling, statistical issues in population biology, systems biology, survival analysis, and multi state models. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields.Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years.

“This book provides a comprehensive overview on statistical analyses of mass spectrometric data. The book aggregates cutting-edge methods developed by established researchers and offers readers opportunities of utilizing mass spectrometry to advance biomedical studies. Statisticians, computer scientists, computational biologists, analytical chemists, and data scientists can benefit from reading this book.” (Hsun-Hsien Chang, Computing Reviews, January, 17 , 2018)​

• Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies.- Automated Alignment of Mass Spectrometry Data Using Functional Geometry.- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution.- Probabilistic and likelihood-based methods for protein identification from MS/MS data.- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing.- Mass Spectrometry Analysis Using MALDIquant.- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry.- The analysis of human serum albumin proteoforms using compositional framework.- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection.- Statistical approach for biomarker discovery using label-free LC-MS data - an overview.- Bayesian posterior integration for classification ofmass spectrometry data.- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies.- Robust and confident predictor selection in metabolomics.- On the combination of omics data for prediction of binary Outcomes.- Statistical analysis of lipidomics data in a case-control study.